¹3 (31) 2017
Demography and social economy, 2017, 3(31):61-75
doi: https://doi.org/10.15407/dse2017.03.061
UDC 005.95/.96, 519.2
JEL CLASSIFICATION: Ñ35, C55, J08, J64
Y.I. YURYK
PhD (Economics), Senior Researcher
Institute for Economic and Forecasting
of the National Academy of Sciences of Ukraine
26, Panasa Myrnoho, Kyiv, 01011, Ukraine
Å-mail: yarina79@ukr.net
G.G. KUZMENKO
PhD (Economics), Expert of Retail Risk Modelling
Alior Bank S.A.
38 D, Lopushanska, Warzsawa, 02-232, Poland
Å-mail: agkuzmenko@ukr.net
THE POSSIBILITIES OF ESTIMATING RISK EVENTS DURING
STRATEGIC MANAGEMENT OF HUMAN RESOURCES
Section: HUMAN RESOURCES MANAGEMENT
Language: English
Abstract: The approach to solution of predicting, classification and risk events diagnosis problems on the labour market
during strategic management of human resources are proposed, which has been tested on assessing the risk of
unemployment among working population of Ukraine. Specifically, the authors has built a scoring model, which
takes into account the joint influence of socio-demographic and professional-and-qualification characteristics
of employees, and calculates points based on which it ranks the employees by the risk of the loss of work. It has
been discovered that a portrait of employee with the highest probability of «bad events» is the following: single
male, aged 15–22, living in rural areas, with profession according to diploma (certificate) – qualified agriculture
and forestry employee, skilled tool worker, person working in maintenance, exploitation and monitoring
of technological equipment, while being employed in another job, mainly performing the simplest tasks in such
economic areas as agriculture and construction.
The scoring model was built using the method of binary logistic regression and the R, SPSS and MS Excel software.
On the basis of the model, one can not only structure the process of preparing possible solutions for risk
management, but also carry out a preliminary assessment of the significance of the employee’s processed characteristics
associated with the likelihood of risk events.
A monitoring of the built scoring model is carried out in order to assess the risk of unemployment among
working population of Ukraine. Based on the testing using such parameters as stability, discriminatory power
(ranking efficiency) and calibration quality, the author confirmed the model’s good predictive ability and adequate
functioning.
The model for estimation of probability of unemployment among the employed population of Ukraine
is presented primarily as an example of scoring application in HR field. The future prospects of creating such
probabilistic models, such tool can be relevant for state institutions, for example employment bureau, as well as employers, i.e. all parties involved in creation and implementation of HR management strategies. From large
amount of data they accumulate on a daily basis the knowledge base for making conscious, not intuitive, strategic
decisions and tactical steps can be obtained.
Key words: strategic management, human resources, risk, scoring model, unemployment.
References:
1. Yurynets, R.V. (2009). Ekonometrychna model otsiniuvannia kredytnoho pozychalnyka vidpovidno do ekspertnoi otsinky [Ekonometric Model of Evaluation of Credit Borrower Accordingly to Expert Estimation]. Naukovyi visnyk NLTU Ukrainy - Scientific Bulletin of Ukrainian National Forestry University, 19.5, 254-258 [in Ukrainian].
2. Abdou, H.A., & Pointon, J. (2011). Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature. Intelligent Systems in Accounting, Finance and Management, 18, 2-3, 59-88. https://doi.org/10.1002/isaf.325
3. Anderson, R. (2007). The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation. Oxford: Oxford University Press.
4. Hand, D.J., & Henley, W.E. (1997). Statistical Classification Methods in Consumer Credit Scoring: A Review. Journal of the Royal Statistical Society: Series A (Statistics in Society), 160, 3, 523-541. https://doi.org/10.1111/j.1467-985X.1997.00078.x
5. Kaminsky, A.B. (2006). Modeliuvannia finansovykh ryzykiv [Modeling of Financial Risk]. Kyiv: Kyiv National Taras Shevchenko University [in Ukrainian].
6. Kaminsky, A.B., & Pysanets, K.K. (2012). Ckorynhovi tekhnolohii v kredytnomu ryzyk-menedzhmenti [Scoring Technologies in Credit Risk-Management]. Biznes Inform - Business Inform, 4, 197-201 [in Ukrainian].
7. Lewis, E.M. (1992). An Introduction to Credit Scoring. San Rafael, CA: Athena Press.
8. Liu, Y. (2001). New Issues in Credit Scoring Application. Research paper 16/2001. Institute of Information Systems. University of Goettingen.
9. Liu, Y. (2002). A Framework of Data Mining Application Process for Credit Scoring. Research paper 01/2002. Institute of Information Systems. University of Goettingen.
10. Liu, Y. (2002). The Evaluation of Classification Models for Credit Scoring. Research paper 02/2002. Institute of Information Systems. University of Goettingen.
11. Mays, E. (Ed). (2001). Handbook of Credit Scoring. Chicago: Glenlake Pub.
12. Siddiqi, N. (2006). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. Hoboken. NJ: Wiley.
13. Sohn, S.Y., Kim, D.H., & Yoon, J.H. (2016). Technology Credit Scoring Model with Fuzzy Logistic Regression. Applied Soft Computing, 43, 150-158. https://doi.org/10.1016/j.asoc.2016.02.025
14. Thomas, L.C. (2000). A Survey of Credit and Behavioural Scoring: Forecasting Financial Risk of Lending to Consumers. International Journal of Forecasting, 16, 2, 149-172. https://doi.org/10.1016/S0169-2070(00)00034-0
15. Thomas, L.C., Crook, J., & Edelman, D. (2002). Credit Scoring & its Applications: Siam Monographs on Mathematical Modeling and Computation. Siam. USA. https://doi.org/10.1137/1.9780898718317
16. Kebebew, E. (2006). Predictors of Single-gland vs Multigland Parathyroid Disease in Primary Hyperparathyroidism. Arch Surg Archives of Surgery, 141, 8, 777-782. https://doi.org/10.1001/archsurg.141.8.777
17. Malthouse, E.C. (1999). Ridge Regression and Direct Marketing Scoring Models. Journal of Interactive Marketing, 13, 4, 10-23. https://doi.org/10.1002/(SICI)1520-6653(199923)13:4<10::AID-DIR2>3.0.CO;2-3
18. Malthouse, E.C. (2001). Assessing the Performance of Direct Marketing Scoring Models, Journal of Interactive Marketing, 15, 1, 49-62. https://doi.org/10.1002/1520-6653(200124)15:1<49::AID-DIR1003>3.0.CO;2-F
19. Milchakov, K.S., & Shebalkov, M.P. (2015). Skoringovyie kartyi v meditsine: obzor i analiz publikatsiy [Scorecards in Medicine: Analytic Review]. Vrach i informatsionnyie tehnologii - Doctor and Information Technology, 1, 71-79 [in Russian].
20. Nadraga, V.I. (2015). Sotsialni ryzyky: sutnist, analiz, mozhlyvosti vplyvu [Social risks: nature, analysis, ability to influence]. Kyiv: Serdyuk V.L. [in Ukrainian].
» pdf